/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    ResidualModelSelection.java
 *    Copyright (C) 2003-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.lmt;

import weka.classifiers.trees.j48.ClassifierSplitModel;
import weka.classifiers.trees.j48.Distribution;
import weka.classifiers.trees.j48.ModelSelection;
import weka.classifiers.trees.j48.NoSplit;
import weka.core.Instances;

/**
 * Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to
 * implement the splitting criterion based on residuals.
 * 
 * @author Niels Landwehr
 * @version $Revision$
 */
public class ResidualModelSelection extends ModelSelection {

    /** for serialization */
    private static final long serialVersionUID = -293098783159385148L;

    /** Minimum number of instances for leaves */
    protected int m_minNumInstances;

    /** Minimum information gain for split */
    protected double m_minInfoGain;

    /**
     * Constructor to create ResidualModelSelection object.
     * 
     * @param minNumInstances minimum number of instances for leaves
     */
    public ResidualModelSelection(int minNumInstances) {
        m_minNumInstances = minNumInstances;
        m_minInfoGain = 1.0E-4;
    }

    /** Method not in use */
    public void cleanup() {
        // method not in use
    }

    /**
     * Selects split based on residuals for the given dataset.
     */
    public final ClassifierSplitModel selectModel(Instances data, double[][] dataZs, double[][] dataWs) throws Exception {

        int numAttributes = data.numAttributes();

        if (numAttributes < 2)
            throw new Exception("Can't select Model without non-class attribute");
        if (data.numInstances() < m_minNumInstances)
            return new NoSplit(new Distribution(data));

        double bestGain = -Double.MAX_VALUE;
        int bestAttribute = -1;

        // try split on every attribute
        for (int i = 0; i < numAttributes; i++) {
            if (i != data.classIndex()) {

                // build split
                ResidualSplit split = new ResidualSplit(i);
                split.buildClassifier(data, dataZs, dataWs);

                if (split.checkModel(m_minNumInstances)) {

                    // evaluate split
                    double gain = split.entropyGain();
                    if (gain > bestGain) {
                        bestGain = gain;
                        bestAttribute = i;
                    }
                }
            }
        }

        if (bestGain >= m_minInfoGain) {
            // return best split
            ResidualSplit split = new ResidualSplit(bestAttribute);
            split.buildClassifier(data, dataZs, dataWs);
            return split;
        } else {
            // could not find any split with enough information gain
            return new NoSplit(new Distribution(data));
        }
    }

    /** Method not in use */
    public final ClassifierSplitModel selectModel(Instances train) {
        // method not in use
        return null;
    }

    /** Method not in use */
    public final ClassifierSplitModel selectModel(Instances train, Instances test) {
        // method not in use
        return null;
    }

}
